Unlocking the value of health data - Presentation at the Congressional Luncheon Series - Oct 2011

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Presentation at the Congressional Luncheon Series - Unlocking the value of Health Data
Citation: Purao, S. 2011. Unlocking the value of health data, Presentation to the congressional staff. Washington, DC. October 2011.

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  • I am Sandeep – Sun–Deep Purao – like Perot but spelled differently as you can see. I am on the faculty of the College of Information at Penn State.   Would like to begin by thanking Neal for inviting me to a part of this panel of presenters. I am glad to be here and am glad to see a large turnout.
  • My mandate today is to talk fairly broadly about the role of data in healthcare. My expertise here is on the word “data” instead of the word “healthcare.” So, with that in mind, I will try to accomplish three goals in the short talk today.
  • First – I would like to add some structure to this large and intractable problem of how to deal with data in the context of healthcare. Structure is important because it allows us the ability to identify different components and focus on the one that we deem are critical.   Second – I would like to point out some ongoing research in select areas that are suggested by this structure. My intent here would be to share with you opportunities where the healthcare community may be able to leverage some of these research results.   Third – I will point out areas where existing research in allied disciplines may not be sufficient or specific for healthcare. This may be one way to distill the arguments in the talk so that efforts and resources may be brought to bear on these areas of research.
  • So, the structure I propose is simple. - The first dimension of the structure is the Data Cycle – A data life cycle acknowledges the phase through which data must flow
  • The second dimension of the structure are the different categories of data one sees in healthcare settings The first two layers are – clinical and administrative software and adds two others – publicly available data and primary data. These are the horizontal layers.   I know this audience knows it well but I will spend a precious minute from the time allocated to me in explicating each. The clinical layer refers to actual patient data such as ailments, medications, vitals etc. The administrative layer refers to data about managing the healthcare delivery system including payments, insurance, schedules etc.   The third layer is public data. Consider, for example, webMD, books – increasingly, digitized, physician ratings etc. We can also include publicly available research outcomes such as those in Pubmed or Medline.   The final layer is primary data. This is data such as clinical trials (see clinicaltrials.gov), data that researchers collect about obesity and exercise patterns, and data about what makes regional health networks work – collected with surveys and interviews.  
  • In each cell, I see several opportunities. In each cell, there are also known problems. I would not be here if we did not know the opportunities. I am here because we realize that there are problems that we must overcome to realize the promise of “data in healthcare.”   So, let me address some key problems – before I do that I cannot resist the temptation to quickly show you how the simple structure can be seen from different perspectives such as - .
  • So a physician may see things differently – as would a patient and a lawyer and a policy maker – These are often implicit – making them open allows us to be clear about where we are focusing
  • So here are some key problems then – I have selected four from a long list
  • Unlocking the value of health data - Presentation at the Congressional Luncheon Series - Oct 2011

    1. 1. 1Unlocking the Value of Health DataSandeep Purao, Ph.D.Research Director, Center for Enterprise ArchitectureAssociate Professor, College of Information Sciences and TechnologyPenn State University, University Park, PACongressional Luncheon Series, 5 October 2011
    2. 2. © Sandeep Purao. spurao@ist.psu.eduMy Emphasis is on ‘Data’2
    3. 3. © Sandeep Purao. spurao@ist.psu.eduI have three Goals today• I want to add structure to the large and intractableproblem of how to deal with data in healthcare• I would like to point out ongoing research in otherdomains based on this structure• I would like to suggest how we may leverage thisresearch or identify opportunities for enhancement3
    4. 4. © Sandeep Purao. spurao@ist.psu.eduA Data (Life) Cycle helps4Generate /CaptureStore /CategorizeAdding structure to the problem of dealing with data in healthcareShare / MakeAvailableUse / MakeSenseDestroy /ShredA data life cycle brings to the foregroundthe phases through which data must flow
    5. 5. © Sandeep Purao. spurao@ist.psu.eduA Layered view of Data helps5Data for the support of Clinical tasks and systemsAdding structure to the problem of dealing with data in healthcareData for the support of Administrative tasks and systemsData that is Public or is available in Public sourcesPrimary Data from Institutions and Researchers
    6. 6. © Sandeep Purao. spurao@ist.psu.eduHere is a Simple Structure6Generate /CaptureStore /CategorizeShare / MakeAvailableUse / MakeSenseDestroy /ShredData for the support of Clinical tasks and systemsData for the support of Administrative tasks and systemsData that is Public or is available in Public sourcesPrimary Data from Institutions and ResearchersAdding structure to the problem of dealing with data in healthcare
    7. 7. © Sandeep Purao. spurao@ist.psu.eduAdding Roles to the Structure7Adding structure to the problem of dealing with data in healthcarePhysiciansPatientsPolicy MakersLawyersResearchers
    8. 8. © Sandeep Purao. spurao@ist.psu.eduThere are some Key Problems8ScaleInter-OperabilityUsing the structure to work with key problems related to data in healthcareSecuritySense-Making
    9. 9. © Sandeep Purao. spurao@ist.psu.eduThere is Research Elsewhere• Scale– Big Data – moving Giga to Tera to Peta– Clouds, Hadoop and Map-Reduce– Extracting data from information **• Inter-operability• Security• Sense-making9Pointing to Ongoing Research in Other Domains
    10. 10. © Sandeep Purao. spurao@ist.psu.eduThere is Research Elsewhere• Scale• Inter-operability– Voluntary and Consensus standards including HL7 **– Heterogeneity, ontology and semantics (NLM) **– Regional health IT partnerships **• Security• Sense-making10Pointing to Ongoing Research in Other Domains
    11. 11. © Sandeep Purao. spurao@ist.psu.eduThere is Research Elsewhere11Pointing to Ongoing Research in Other Domains• Scale• Inter-operability• Security– Dealing with legal co-existence of malicious users **– Measures such as Role-based Access– Laws to prevent access to EHR **• Sense-making
    12. 12. © Sandeep Purao. spurao@ist.psu.eduThere is Research Elsewhere12Pointing to Ongoing Research in Other Domains• Scale• Inter-operability• Security• Sense-Making– Measuring data quality in crowd-based forums **– Search patterns and user behaviors **– Data delivery / use for e-health
    13. 13. © Sandeep Purao. spurao@ist.psu.eduOpportunities• It is possible to leverage / enhance research from otherdomains to add to what we know about the Data Puzzlein the Healthcare context13Leveraging or Enhancing Existing Research
    14. 14. © Sandeep Purao. spurao@ist.psu.eduSome Examples - 1• Example 1: Regional health partnerships– A study of regional health IT partnerships extending ideas andtheories about outsourcing– Problem addressed: Data storage and Data sharing• Example 2: Extracting action knowledge– Studies of work in refineries and with health professionals toextract and represent action knowledge– Problem addressed: Data use and sense-making14Leveraging or Enhancing Existing Research
    15. 15. © Sandeep Purao. spurao@ist.psu.eduSome Examples - 2• Example 3: Changing models for data governance with clouds– A study of healthcare organizations to understand how datastewardship and governance models are changing with cloud– Problem addressed: Data storage and exchange• Example 4: Using context to overcome data heterogeneity– Modeling context to understand how data may be exchangedacross different user communities– Problem addressed: Data Sharing and Data use15Leveraging or Enhancing Existing Research
    16. 16. © Sandeep Purao. spurao@ist.psu.eduSome Examples - 3• Example 5: Data use in collaborative healthcare teams– A study to understand how teams of healthcare professionalsaccess and use data– Problem addressed: Data sharing and Data use• Example 6: Search patterns for specialized information on the web– Empirical analyses of how user communities look for health-related and other information on web sources– Problem addressed: Data Access and Data use16Leveraging or Enhancing Existing Research
    17. 17. © Sandeep Purao. spurao@ist.psu.eduWhat I said I wanted to do• I want to add structure to the large and intractable problem of howto deal with data in healthcare• I would like to point out ongoing research in other domains basedon this structure• I would like to suggest how we may leverage this research oridentify opportunities for enhancement17
    18. 18. © Sandeep Purao. spurao@ist.psu.eduSummary18For further dialog: spurao@ist.psu.edu

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